Coverage for python/lsst/pipe/tasks/isolatedStarAssociation.py: 14%
213 statements
« prev ^ index » next coverage.py v6.5.0, created at 2022-11-03 01:34 -0700
« prev ^ index » next coverage.py v6.5.0, created at 2022-11-03 01:34 -0700
1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ['IsolatedStarAssociationConnections',
23 'IsolatedStarAssociationConfig',
24 'IsolatedStarAssociationTask']
26import numpy as np
27import pandas as pd
28from smatch.matcher import Matcher
30import lsst.pex.config as pexConfig
31import lsst.pipe.base as pipeBase
32from lsst.skymap import BaseSkyMap
33from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
36class IsolatedStarAssociationConnections(pipeBase.PipelineTaskConnections,
37 dimensions=('instrument', 'tract', 'skymap',),
38 defaultTemplates={}):
39 source_table_visit = pipeBase.connectionTypes.Input(
40 doc='Source table in parquet format, per visit',
41 name='sourceTable_visit',
42 storageClass='DataFrame',
43 dimensions=('instrument', 'visit'),
44 deferLoad=True,
45 multiple=True,
46 )
47 skymap = pipeBase.connectionTypes.Input(
48 doc="Input definition of geometry/bbox and projection/wcs for warped exposures",
49 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
50 storageClass='SkyMap',
51 dimensions=('skymap',),
52 )
53 isolated_star_sources = pipeBase.connectionTypes.Output(
54 doc='Catalog of individual sources for the isolated stars',
55 name='isolated_star_sources',
56 storageClass='DataFrame',
57 dimensions=('instrument', 'tract', 'skymap'),
58 )
59 isolated_star_cat = pipeBase.connectionTypes.Output(
60 doc='Catalog of isolated star positions',
61 name='isolated_star_cat',
62 storageClass='DataFrame',
63 dimensions=('instrument', 'tract', 'skymap'),
64 )
67class IsolatedStarAssociationConfig(pipeBase.PipelineTaskConfig,
68 pipelineConnections=IsolatedStarAssociationConnections):
69 """Configuration for IsolatedStarAssociationTask."""
71 inst_flux_field = pexConfig.Field(
72 doc=('Full name of instFlux field to use for s/n selection and persistence. '
73 'The associated flag will be implicity included in bad_flags. '
74 'Note that this is expected to end in ``instFlux``.'),
75 dtype=str,
76 default='apFlux_12_0_instFlux',
77 )
78 match_radius = pexConfig.Field(
79 doc='Match radius (arcseconds)',
80 dtype=float,
81 default=1.0,
82 )
83 isolation_radius = pexConfig.Field(
84 doc=('Isolation radius (arcseconds). Any stars with average centroids '
85 'within this radius of another star will be rejected from the final '
86 'catalog. This radius should be at least 2x match_radius.'),
87 dtype=float,
88 default=2.0,
89 )
90 band_order = pexConfig.ListField(
91 doc=(('Ordered list of bands to use for matching/storage. '
92 'Any bands not listed will not be matched.')),
93 dtype=str,
94 default=['i', 'z', 'r', 'g', 'y', 'u'],
95 )
96 id_column = pexConfig.Field(
97 doc='Name of column with source id.',
98 dtype=str,
99 default='sourceId',
100 )
101 ra_column = pexConfig.Field(
102 doc='Name of column with right ascension.',
103 dtype=str,
104 default='ra',
105 )
106 dec_column = pexConfig.Field(
107 doc='Name of column with declination.',
108 dtype=str,
109 default='decl',
110 )
111 physical_filter_column = pexConfig.Field(
112 doc='Name of column with physical filter name',
113 dtype=str,
114 default='physical_filter',
115 )
116 band_column = pexConfig.Field(
117 doc='Name of column with band name',
118 dtype=str,
119 default='band',
120 )
121 extra_columns = pexConfig.ListField(
122 doc='Extra names of columns to read and persist (beyond instFlux and error).',
123 dtype=str,
124 default=['x',
125 'y',
126 'apFlux_17_0_instFlux',
127 'apFlux_17_0_instFluxErr',
128 'apFlux_17_0_flag',
129 'localBackground_instFlux',
130 'localBackground_flag']
131 )
132 source_selector = sourceSelectorRegistry.makeField(
133 doc='How to select sources. Under normal usage this should not be changed.',
134 default='science'
135 )
137 def setDefaults(self):
138 super().setDefaults()
140 source_selector = self.source_selector['science']
141 source_selector.setDefaults()
143 source_selector.doFlags = True
144 source_selector.doUnresolved = True
145 source_selector.doSignalToNoise = True
146 source_selector.doIsolated = True
148 source_selector.signalToNoise.minimum = 10.0
149 source_selector.signalToNoise.maximum = 1000.0
151 flux_flag_name = self.inst_flux_field.replace("instFlux", "flag")
153 source_selector.flags.bad = ['pixelFlags_edge',
154 'pixelFlags_interpolatedCenter',
155 'pixelFlags_saturatedCenter',
156 'pixelFlags_crCenter',
157 'pixelFlags_bad',
158 'pixelFlags_interpolated',
159 'pixelFlags_saturated',
160 'centroid_flag',
161 flux_flag_name]
163 source_selector.signalToNoise.fluxField = self.inst_flux_field
164 source_selector.signalToNoise.errField = self.inst_flux_field + 'Err'
166 source_selector.isolated.parentName = 'parentSourceId'
167 source_selector.isolated.nChildName = 'deblend_nChild'
169 source_selector.unresolved.maximum = 0.5
170 source_selector.unresolved.name = 'extendedness'
173class IsolatedStarAssociationTask(pipeBase.PipelineTask):
174 """Associate sources into isolated star catalogs.
175 """
176 ConfigClass = IsolatedStarAssociationConfig
177 _DefaultName = 'isolatedStarAssociation'
179 def __init__(self, **kwargs):
180 super().__init__(**kwargs)
182 self.makeSubtask('source_selector')
183 # Only log warning and fatal errors from the source_selector
184 self.source_selector.log.setLevel(self.source_selector.log.WARN)
186 def runQuantum(self, butlerQC, inputRefs, outputRefs):
187 input_ref_dict = butlerQC.get(inputRefs)
189 tract = butlerQC.quantum.dataId['tract']
191 source_table_refs = input_ref_dict['source_table_visit']
193 self.log.info('Running with %d source_table_visit dataRefs',
194 len(source_table_refs))
196 source_table_ref_dict_temp = {source_table_ref.dataId['visit']: source_table_ref for
197 source_table_ref in source_table_refs}
199 bands = {source_table_ref.dataId['band'] for source_table_ref in source_table_refs}
200 for band in bands:
201 if band not in self.config.band_order:
202 self.log.warning('Input data has data from band %s but that band is not '
203 'configured for matching', band)
205 # TODO: Sort by visit until DM-31701 is done and we have deterministic
206 # dataset ordering.
207 source_table_ref_dict = {visit: source_table_ref_dict_temp[visit] for
208 visit in sorted(source_table_ref_dict_temp.keys())}
210 struct = self.run(input_ref_dict['skymap'], tract, source_table_ref_dict)
212 butlerQC.put(pd.DataFrame(struct.star_source_cat),
213 outputRefs.isolated_star_sources)
214 butlerQC.put(pd.DataFrame(struct.star_cat),
215 outputRefs.isolated_star_cat)
217 def run(self, skymap, tract, source_table_ref_dict):
218 """Run the isolated star association task.
220 Parameters
221 ----------
222 skymap : `lsst.skymap.SkyMap`
223 Skymap object.
224 tract : `int`
225 Tract number.
226 source_table_ref_dict : `dict`
227 Dictionary of source_table refs. Key is visit, value is dataref.
229 Returns
230 -------
231 struct : `lsst.pipe.base.struct`
232 Struct with outputs for persistence.
233 """
234 star_source_cat = self._make_all_star_sources(skymap[tract], source_table_ref_dict)
236 primary_bands = self.config.band_order
238 # Do primary matching
239 primary_star_cat = self._match_primary_stars(primary_bands, star_source_cat)
241 if len(primary_star_cat) == 0:
242 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
243 star_cat=np.zeros(0, primary_star_cat.dtype))
245 # Remove neighbors
246 primary_star_cat = self._remove_neighbors(primary_star_cat)
248 if len(primary_star_cat) == 0:
249 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
250 star_cat=np.zeros(0, primary_star_cat.dtype))
252 # Crop to inner tract region
253 inner_tract_ids = skymap.findTractIdArray(primary_star_cat[self.config.ra_column],
254 primary_star_cat[self.config.dec_column],
255 degrees=True)
256 use = (inner_tract_ids == tract)
257 self.log.info('Total of %d isolated stars in inner tract.', use.sum())
259 primary_star_cat = primary_star_cat[use]
261 if len(primary_star_cat) == 0:
262 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
263 star_cat=np.zeros(0, primary_star_cat.dtype))
265 # Set the unique ids.
266 primary_star_cat['isolated_star_id'] = self._compute_unique_ids(skymap,
267 tract,
268 len(primary_star_cat))
270 # Match to sources.
271 star_source_cat, primary_star_cat = self._match_sources(primary_bands,
272 star_source_cat,
273 primary_star_cat)
275 return pipeBase.Struct(star_source_cat=star_source_cat,
276 star_cat=primary_star_cat)
278 def _make_all_star_sources(self, tract_info, source_table_ref_dict):
279 """Make a catalog of all the star sources.
281 Parameters
282 ----------
283 tract_info : `lsst.skymap.TractInfo`
284 Information about the tract.
285 source_table_ref_dict : `dict`
286 Dictionary of source_table refs. Key is visit, value is dataref.
288 Returns
289 -------
290 star_source_cat : `np.ndarray`
291 Catalog of star sources.
292 """
293 # Internally, we use a numpy recarray, they are by far the fastest
294 # option in testing for relatively narrow tables.
295 # (have not tested wide tables)
296 all_columns, persist_columns = self._get_source_table_visit_column_names()
297 poly = tract_info.outer_sky_polygon
299 tables = []
300 for visit in source_table_ref_dict:
301 source_table_ref = source_table_ref_dict[visit]
302 df = source_table_ref.get(parameters={'columns': all_columns})
303 df.reset_index(inplace=True)
305 goodSrc = self.source_selector.selectSources(df)
307 table = df[persist_columns][goodSrc.selected].to_records()
309 # Append columns that include the row in the source table
310 # and the matched object index (to be filled later).
311 table = np.lib.recfunctions.append_fields(table,
312 ['source_row',
313 'obj_index'],
314 [np.where(goodSrc.selected)[0],
315 np.zeros(goodSrc.selected.sum(), dtype=np.int32)],
316 dtypes=['i4', 'i4'],
317 usemask=False)
319 # We cut to the outer tract polygon to ensure consistent matching
320 # from tract to tract.
321 tract_use = poly.contains(np.deg2rad(table[self.config.ra_column]),
322 np.deg2rad(table[self.config.dec_column]))
324 tables.append(table[tract_use])
326 # Combine tables
327 star_source_cat = np.concatenate(tables)
329 return star_source_cat
331 def _get_source_table_visit_column_names(self):
332 """Get the list of sourceTable_visit columns from the config.
334 Returns
335 -------
336 all_columns : `list` [`str`]
337 All columns to read
338 persist_columns : `list` [`str`]
339 Columns to persist (excluding selection columns)
340 """
341 columns = [self.config.id_column,
342 'visit', 'detector',
343 self.config.ra_column, self.config.dec_column,
344 self.config.physical_filter_column, self.config.band_column,
345 self.config.inst_flux_field, self.config.inst_flux_field + 'Err']
346 columns.extend(self.config.extra_columns)
348 all_columns = columns.copy()
349 if self.source_selector.config.doFlags:
350 all_columns.extend(self.source_selector.config.flags.bad)
351 if self.source_selector.config.doUnresolved:
352 all_columns.append(self.source_selector.config.unresolved.name)
353 if self.source_selector.config.doIsolated:
354 all_columns.append(self.source_selector.config.isolated.parentName)
355 all_columns.append(self.source_selector.config.isolated.nChildName)
357 return all_columns, columns
359 def _match_primary_stars(self, primary_bands, star_source_cat):
360 """Match primary stars.
362 Parameters
363 ----------
364 primary_bands : `list` [`str`]
365 Ordered list of primary bands.
366 star_source_cat : `np.ndarray`
367 Catalog of star sources.
369 Returns
370 -------
371 primary_star_cat : `np.ndarray`
372 Catalog of primary star positions
373 """
374 ra_col = self.config.ra_column
375 dec_col = self.config.dec_column
377 dtype = self._get_primary_dtype(primary_bands)
379 primary_star_cat = None
380 for primary_band in primary_bands:
381 use = (star_source_cat['band'] == primary_band)
383 ra = star_source_cat[ra_col][use]
384 dec = star_source_cat[dec_col][use]
386 with Matcher(ra, dec) as matcher:
387 try:
388 # New smatch API
389 idx = matcher.query_groups(self.config.match_radius/3600., min_match=1)
390 except AttributeError:
391 # Old smatch API
392 idx = matcher.query_self(self.config.match_radius/3600., min_match=1)
394 count = len(idx)
396 if count == 0:
397 self.log.info('Found 0 primary stars in %s band.', primary_band)
398 continue
400 band_cat = np.zeros(count, dtype=dtype)
401 band_cat['primary_band'] = primary_band
403 # If the tract cross ra=0 (that is, it has both low ra and high ra)
404 # then we need to remap all ra values from [0, 360) to [-180, 180)
405 # before doing any position averaging.
406 remapped = False
407 if ra.min() < 60.0 and ra.max() > 300.0:
408 ra_temp = (ra + 180.0) % 360. - 180.
409 remapped = True
410 else:
411 ra_temp = ra
413 # Compute mean position for each primary star
414 for i, row in enumerate(idx):
415 row = np.array(row)
416 band_cat[ra_col][i] = np.mean(ra_temp[row])
417 band_cat[dec_col][i] = np.mean(dec[row])
419 if remapped:
420 # Remap ra back to [0, 360)
421 band_cat[ra_col] %= 360.0
423 # Match to previous band catalog(s), and remove duplicates.
424 if primary_star_cat is None or len(primary_star_cat) == 0:
425 primary_star_cat = band_cat
426 else:
427 with Matcher(band_cat[ra_col], band_cat[dec_col]) as matcher:
428 idx = matcher.query_radius(primary_star_cat[ra_col],
429 primary_star_cat[dec_col],
430 self.config.match_radius/3600.)
431 # Any object with a match should be removed.
432 match_indices = np.array([i for i in range(len(idx)) if len(idx[i]) > 0])
433 if len(match_indices) > 0:
434 band_cat = np.delete(band_cat, match_indices)
436 primary_star_cat = np.append(primary_star_cat, band_cat)
437 self.log.info('Found %d primary stars in %s band.', len(band_cat), primary_band)
439 # If everything was cut, we still want the correct datatype.
440 if primary_star_cat is None:
441 primary_star_cat = np.zeros(0, dtype=dtype)
443 return primary_star_cat
445 def _remove_neighbors(self, primary_star_cat):
446 """Remove neighbors from the primary star catalog.
448 Parameters
449 ----------
450 primary_star_cat : `np.ndarray`
451 Primary star catalog.
453 Returns
454 -------
455 primary_star_cat_cut : `np.ndarray`
456 Primary star cat with neighbors removed.
457 """
458 ra_col = self.config.ra_column
459 dec_col = self.config.dec_column
461 with Matcher(primary_star_cat[ra_col], primary_star_cat[dec_col]) as matcher:
462 # By setting min_match=2 objects that only match to themselves
463 # will not be recorded.
464 try:
465 # New smatch API
466 idx = matcher.query_groups(self.config.isolation_radius/3600., min_match=2)
467 except AttributeError:
468 # Old smatch API
469 idx = matcher.query_self(self.config.isolation_radius/3600., min_match=2)
471 try:
472 neighbor_indices = np.concatenate(idx)
473 except ValueError:
474 neighbor_indices = np.zeros(0, dtype=int)
476 if len(neighbor_indices) > 0:
477 neighbored = np.unique(neighbor_indices)
478 self.log.info('Cutting %d objects with close neighbors.', len(neighbored))
479 primary_star_cat = np.delete(primary_star_cat, neighbored)
481 return primary_star_cat
483 def _match_sources(self, bands, star_source_cat, primary_star_cat):
484 """Match individual sources to primary stars.
486 Parameters
487 ----------
488 bands : `list` [`str`]
489 List of bands.
490 star_source_cat : `np.ndarray`
491 Array of star sources.
492 primary_star_cat : `np.ndarray`
493 Array of primary stars.
495 Returns
496 -------
497 star_source_cat_sorted : `np.ndarray`
498 Sorted and cropped array of star sources.
499 primary_star_cat : `np.ndarray`
500 Catalog of isolated stars, with indexes to star_source_cat_cut.
501 """
502 ra_col = self.config.ra_column
503 dec_col = self.config.dec_column
505 # We match sources per-band because it allows us to have sorted
506 # sources for easy retrieval of per-band matches.
507 n_source_per_band_per_obj = np.zeros((len(bands),
508 len(primary_star_cat)),
509 dtype=np.int32)
510 band_uses = []
511 idxs = []
512 with Matcher(primary_star_cat[ra_col], primary_star_cat[dec_col]) as matcher:
513 for b, band in enumerate(bands):
514 band_use, = np.where(star_source_cat['band'] == band)
516 idx = matcher.query_radius(star_source_cat[ra_col][band_use],
517 star_source_cat[dec_col][band_use],
518 self.config.match_radius/3600.)
519 n_source_per_band_per_obj[b, :] = np.array([len(row) for row in idx])
520 idxs.append(idx)
521 band_uses.append(band_use)
523 n_source_per_obj = np.sum(n_source_per_band_per_obj, axis=0)
525 primary_star_cat['nsource'] = n_source_per_obj
526 primary_star_cat['source_cat_index'][1:] = np.cumsum(n_source_per_obj)[:-1]
528 n_tot_source = primary_star_cat['source_cat_index'][-1] + primary_star_cat['nsource'][-1]
530 # Temporary arrays until we crop/sort the source catalog
531 source_index = np.zeros(n_tot_source, dtype=np.int32)
532 obj_index = np.zeros(n_tot_source, dtype=np.int32)
534 ctr = 0
535 for i in range(len(primary_star_cat)):
536 obj_index[ctr: ctr + n_source_per_obj[i]] = i
537 for b in range(len(bands)):
538 source_index[ctr: ctr + n_source_per_band_per_obj[b, i]] = band_uses[b][idxs[b][i]]
539 ctr += n_source_per_band_per_obj[b, i]
541 source_cat_index_band_offset = np.cumsum(n_source_per_band_per_obj, axis=0)
543 for b, band in enumerate(bands):
544 primary_star_cat[f'nsource_{band}'] = n_source_per_band_per_obj[b, :]
545 if b == 0:
546 # The first band listed is the same as the overall star
547 primary_star_cat[f'source_cat_index_{band}'] = primary_star_cat['source_cat_index']
548 else:
549 # Other band indices are offset from the previous band
550 primary_star_cat[f'source_cat_index_{band}'] = (primary_star_cat['source_cat_index']
551 + source_cat_index_band_offset[b - 1, :])
553 star_source_cat = star_source_cat[source_index]
554 star_source_cat['obj_index'] = obj_index
556 return star_source_cat, primary_star_cat
558 def _compute_unique_ids(self, skymap, tract, nstar):
559 """Compute unique star ids.
561 This is a simple hash of the tract and star to provide an
562 id that is unique for a given processing.
564 Parameters
565 ----------
566 skymap : `lsst.skymap.Skymap`
567 Skymap object.
568 tract : `int`
569 Tract id number.
570 nstar : `int`
571 Number of stars.
573 Returns
574 -------
575 ids : `np.ndarray`
576 Array of unique star ids.
577 """
578 # The end of the id will be big enough to hold the tract number
579 mult = 10**(int(np.log10(len(skymap))) + 1)
581 return (np.arange(nstar) + 1)*mult + tract
583 def _get_primary_dtype(self, primary_bands):
584 """Get the numpy datatype for the primary star catalog.
586 Parameters
587 ----------
588 primary_bands : `list` [`str`]
589 List of primary bands.
591 Returns
592 -------
593 dtype : `numpy.dtype`
594 Datatype of the primary catalog.
595 """
596 max_len = max([len(primary_band) for primary_band in primary_bands])
598 dtype = [('isolated_star_id', 'i8'),
599 (self.config.ra_column, 'f8'),
600 (self.config.dec_column, 'f8'),
601 ('primary_band', f'U{max_len}'),
602 ('source_cat_index', 'i4'),
603 ('nsource', 'i4')]
605 for band in primary_bands:
606 dtype.append((f'source_cat_index_{band}', 'i4'))
607 dtype.append((f'nsource_{band}', 'i4'))
609 return dtype